Vehicle damage detection

ABSTRACT

A damage quantifier for a vehicle component formed of composite material is determined based on vehicle sensor data. The vehicle is operated based on a mission determined according to determined damage quantifier.

BACKGROUND

Carbon Fiber Reinforced Plastics (CFRPs) can be used, e.g., tomanufacture machine parts such as vehicle body parts. CFRPs aretypically used for improved strength, stiffness, tolerance, highresistance to corrosion, weight saving, etc. However, CFRP parts canbecome degraded or damaged, preventing a machine such as vehicle fromoperating, or prevent safe and/or efficient operation of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example vehicle.

FIG. 2A is a side view of a body component of the vehicle of FIG. 1,illustrating example stress areas based on a front impact loading mode.

FIG. 2B is a side view of the body component of the vehicle,illustrating an example stress area based on a top impact loading mode.

FIG. 3A is a diagram showing example degradation or damage to a CFRPpart including a debonding, rupture, pull out, and bridging of afiber-reinforced material.

FIG. 3B is a diagram showing delamination damage of the fiber-reinforcedmaterial.

FIG. 4 shows multiple exemplary graphs of strain response of an examplevehicle component.

FIG. 5A is a side view of the vehicle component with uniformlydistributed sensors to detect component damage.

FIG. 5B is an exemplary side view of the vehicle component with selectedsensor locations.

FIG. 6 shows an example graph of a vehicle safety rating and damagequantifier.

FIG. 7 shows the example component with multiple example damagelocations.

FIG. 8 shows an example graph of stress to the vehicle component versuschanges of environmental conditions.

FIG. 9 shows multiple exemplary graphs of a vehicle component damagequantifier with respect to an environmental parameter.

FIG. 10 is an example exploded diagram of the vehicle of FIG. 1 withdamage in multiple body components.

FIG. 11 is a flowchart of an exemplary process for determining optimizedsensor locations.

FIG. 12 shows an exemplary flowchart for performing vehicle operation.

FIG. 13 shows an exemplary flowchart for re-training a neural networkmodel.

DETAILED DESCRIPTION

Introduction

Disclosed herein is a method including determining a damage quantifierfor a vehicle component formed of composite material based on vehiclesensor data, and operating the vehicle based on a mission determinedaccording to determined damage quantifier.

Determining the mission may include selecting, based on the determineddamage quantifier, a mode of navigation from at least one of cargo-only,cargo and passenger, move with no cargo or passenger, stop movement.

The method may further include selecting the mode of navigation based ona plurality of damage quantifier thresholds.

The method may further include predicting a change of the damagequantifier based on a planned route of the vehicle.

The method may further include predicting the change of the damagequantifier based on data environmental sensor data including at leastone of a vehicle speed, an ambient temperature, and an ambient humidity.

The method may further include determining the damage quantifier basedon a model that takes the received data from the sensors included in thevehicle component as input, and outputs the damage quantifier for therespective vehicle component.

The method may further include identifying stress locations in thevehicle components for one or more types of loading mode of the vehiclecomponent, identifying a plurality of sensor locations in the vehiclecomponent for the plurality of sensors based on the identified stresslocations, and mounting the plurality of the sensors at the identifiedsensor locations.

The types of loading modes may include at least one of a roof impact, afrontal impact, a rear impact, and a side impact.

The method may further include selecting one or more types of loadingmodes for detection, and identifying the plurality of sensor locationsbased on the selected one or more types of loading modes.

Further disclosed herein is a system including a processor and a memory.The memory stores instructions executable by the processor to determinea damage quantifier for a vehicle component formed of composite materialbased on vehicle sensor data, and operate the vehicle based on a missiondetermined according to determined damage quantifier.

The instructions to determine the mission may further includeinstructions to select, based on the determined damage quantifier, amode of navigation from at least one of cargo-only, cargo and passenger,move with no cargo or passenger, stop movement.

The instructions may further include instructions to select the mode ofnavigation based on a plurality of damage quantifier thresholds.

The instructions may further include instructions to predict a change ofthe damage quantifier based on a planned route of the vehicle.

The instructions may further include instructions to predict the changeof the damage quantifier based on data environmental sensor dataincluding at least one of a vehicle speed, an ambient temperature, andan ambient humidity.

The instructions may further include instructions to determine thedamage quantifier based on a model that takes the received data from thesensors included in the vehicle component as input, and outputs thedamage quantifier for the respective vehicle component.

The instructions may further include instructions to identify stresslocations in the vehicle components for one or more types of loadingmode of the vehicle component, and identify a plurality of sensorlocations in the vehicle component for the plurality of sensors based onthe identified stress locations.

The types of loading modes may include at least one of a roof impact, afrontal impact, a rear impact, and a side impact.

The instructions may further include instructions to select one or moretypes of loading modes for detection, and identify the plurality ofsensor locations based on the selected one or more types of loadingmodes.

Further disclosed herein is a system including means for determining adamage quantifier for a vehicle component formed of composite materialbased on vehicle sensor data, and means for operating the vehicle basedon a mission determined according to determined damage quantifier.

The system may further include means for identifying stress locations inthe vehicle components for one or more types of loading mode of thevehicle component, means for identifying a plurality of sensor locationsin the vehicle component for the plurality of sensors based on theidentified stress locations, and means for mounting the plurality of thesensors at the identified sensor locations.

Further disclosed is a computing device programmed to execute any of theabove method steps.

Yet further disclosed is a computer program product, comprising acomputer readable medium storing instructions executable by a computerprocessor, to execute any of the above method steps.

Exemplary System Elements

A vehicle computer may be programmed to determine a damage quantifierfor a vehicle component formed of composite material based on vehiclesensor data, and to operate the vehicle according to a determined damagequantifier. The computer may be programmed to alter a vehicle mission(e.g., a mode of navigation, vehicle path, etc.) based on detecteddamage, e.g., to reduce risk of injury, further damage, etc., e.g.,because of a risk or occurrence of a structural failure or diminishedcrash worthiness. In one example, the vehicle computer may also actuatea vehicle actuator, e.g., a communication device, to request repair at aservice center based on detected damage.

FIG. 1 illustrates a vehicle 100. The vehicle 100 may be powered in avariety of known ways, e.g., with an electric motor and/or internalcombustion engine. The vehicle 100 may be a land vehicle such as a car,a truck, a drone, etc. A vehicle 100 may include a computer 110,actuator(s) 120, sensor(s) 130, a human machine interface (HMI 140), areference point, and a body 160. A vehicle 100 reference point 150 is aspecified point within the space defined by the vehicle body 160, e.g.,a geometrical center point at which respective longitudinal and lateralcenter axes of the vehicle 100 intersect.

The computer 110 includes a processor and a memory such as are known.The memory includes one or more forms of computer-readable media, andstores instructions executable by the computer 110 for performingvarious operations, including as disclosed herein.

The computer 110 may operate the vehicle 100 in an autonomous or asemi-autonomous mode. For purposes of this disclosure, an autonomousmode is defined as one in which each of vehicle 100 propulsion, braking,and steering are controlled by the computer 110; in a semi-autonomousmode the computer 110 controls one or two of vehicles 100 propulsion,braking, and steering.

The computer 110 may include programming to operate one or more landvehicle brakes, propulsion (e.g., control of acceleration in the vehicleby controlling one or more of an internal combustion engine, electricmotor, hybrid engine, etc.), steering, climate control, interior and/orexterior lights, etc., as well as to determine whether and when thecomputer 110, as opposed to a human operator, is to control suchoperations. Additionally, the computer 110 may be programmed todetermine whether and when a human operator is to control suchoperations.

The computer 110 may include or be communicatively coupled to, e.g., viaa vehicle 100 communications bus as described further below, more thanone processor, e.g., controllers or the like included in the vehicle formonitoring and/or controlling various vehicle controllers, e.g., apowertrain controller, a brake controller, a steering controller, etc.The computer 110 is generally arranged for communications on a vehiclecommunication network that can include a bus in the vehicle such as acontroller area network (CAN) or the like, and/or other wired and/orwireless mechanisms.

Via the vehicle 100 network, the computer 110 may transmit messages tovarious devices in the vehicle and/or receive messages from the variousdevices, e.g., an actuator 120, an HMI 140, etc. Alternatively oradditionally, in cases where the computer 110 actually comprisesmultiple devices, the vehicle 100 communication network may be used forcommunications between devices represented as the computer 110 in thisdisclosure. Further, as mentioned below, various controllers and/orsensors may provide data to the computer 110 via the vehiclecommunication network.

In addition, the computer 110 may be configured for communicatingthrough a wireless vehicular communication interface with other trafficparticipants (e.g., vehicles, infrastructure, pedestrian, etc.), e.g.,via a vehicle-to-vehicle communication network and/or avehicle-to-infrastructure communication network. The vehicularcommunication network represents one or more mechanisms by which thecomputers 110 of vehicles 100 may communicate with other trafficparticipants, and may be one or more of wireless communicationmechanisms, including any desired combination of wireless (e.g.,cellular, wireless, satellite, microwave and radio frequency)communication mechanisms and any desired network topology (or topologieswhen multiple communication mechanisms are utilized). Exemplaryvehicular communication networks include cellular, Bluetooth, IEEE802.11, dedicated short range communications (DSRC), and/or wide areanetworks (WAN), including the Internet, providing data communicationservices.

The vehicle 100 actuators 120 are implemented via circuits, chips, orother electronic and or mechanical components that can actuate variousvehicle subsystems in accordance with appropriate control signals as isknown. The actuators 120 may be used to control braking, acceleration,and steering of the vehicles 100.

The sensors 130 may include a variety of devices known to provide datato the computer 110. For example, one or more object detection sensors130, e.g., radar, camera, lidar (Light Detection and Ranging), etc.,fixed to vehicle 100 body 160 may provide locations of objects relativeto the location of the vehicle 100. The sensors 130 may include camerasensor(s) 130, e.g. to provide a front view, side view, etc., providingimages from an area surrounding the vehicle 100. For example, thecomputer 110 may be programmed to receive image data from a camerasensor(s) 130 and to implement image processing techniques to detect aroad, lane markings, etc. The computer 110 may be further programmed todetermine a current vehicle 100 location based on location coordinates,e.g., GPS coordinates, received from a vehicle 100 location (e.g., GPS)sensor 130. As another example, the computer 110 may be programmed toreceive data including relative speed, location coordinates, and/orheading of other vehicles via the wireless communication network.Further, the vehicle 100 may include damage detection sensors 130included vehicle 100 body 160 to detect various types of damages of body160 component(s) 200, as discussed with respect to FIGS. 5A-5B.

The HMI 140 may be configured to receive information from a user, suchas a human operator, during operation of the vehicle 100. Moreover, aHMI 140 may be configured to present information to the user. Thus, aHMI 140 may be located in the passenger compartment of the vehicle 100.In one example, the computer 110 may be programmed to output a messageto the HMI 140 indicating a restriction of vehicle 100 operation, e.g.,cargo transport only, navigation to nearest service center, etc., asdiscussed throughout this disclosure, e.g., FIG. 12.

The vehicle 100 may have a unibody construction (or monocoque), i.e., aunitary-body construction. In a unibody construction, the body 160serves as a vehicle frame, and the body 160 (including the rockers,pillars, roof rails, etc.) is unitary, i.e., a continuous one-pieceunit. The vehicle 100 body 160 may include a passenger compartment andan engine compartment. The body 160 (interior and/or exterior) may beformed of any suitable material, for example, metal, plastic, and/orfiber-reinforced material. At least a part of the vehicle 100 body canbe formed of CFRP. For example, a substantial part of the body 160 maybe formed of CFRPs a part which may be referred to as a carbon fibermonocoque. Additionally or alternatively, other vehicle 100 components200 such as exterior parts may be formed of carbon fiber.

A carbon fiber reinforced polymer (CFRP), carbon fiber reinforcedplastic or carbon fiber reinforced thermoplastic or often simply carbonfiber or carbon composite, all as used herein have plain and ordinarymeanings that refer to an extremely strong and light fiber-reinforcedplastic which contains carbon fibers. The binding polymer is often athermoset resin such as epoxy, but other thermoset or thermoplasticpolymers, such as polyester, vinyl ester or nylon, are sometimes used.The composite may contain aramid (e.g. Kevlar®, Twaron®), aluminum,ultra-high-molecular-weight polyethylene or glass fibers in addition tocarbon fibers. The properties of a CFRP material can also be based on atype of additive introduced to the binding matrix (the resin). The mostfrequent additive is silica, but other additives such as rubber andcarbon nanotubes can be used. The material may also be referred to asgraphite-reinforced polymer or graphite fiber-reinforced polymer.

The vehicle 100 body 160 may be exposed to various “loading modes.” Inthe present context, a “loading mode” specifies a direction of applyingforce to the body 160, e.g., side impact, frontal impact, top impact,etc., and/or an area of impact, e.g., roof, doors, front bumper, rearbumper, etc. Vehicles, such as vehicle 100, are subject to variousstandards related to various loading modes of the vehicles 100 asdefined by Federal Motor Vehicle Safety Standards (FMVSS), InsuranceInstitute for Highway Safety (IIHS) standards, EURO NCAP (New CarAssessment Program), and/or NHTSA (National Highway TrafficAdministration). A frontal impact may include, for example, head-onimpact, angular frontal impact, small offset rigid barrier (SORB)impact, etc.

FIGS. 2A-2B illustrate an example body component 200, e.g., a frontpillar assembly, in different loading modes. FIG. 2A shows the bodycomponent 200 with a first loading mode, e.g., front impact conditionwith an energy path P₁. The vehicle 100 may move in a travel directionT. In one example, a front impact, e.g., a frontal crash, is applied tothe vehicle 100 in a direction substantially opposite to the traveldirection T, and energy absorbed by the body 160 may travel in adirection of the energy path P₁. FIG. 2B shows the body component 200with a second loading mode, e.g., roof impact condition with an energypath P₂. In one example, a roof impact (e.g., as a result of vehicle 100roll over) may be applied to the vehicle 100 in a directionsubstantially vertical to the travel direction T, and energy absorbed bythe component 200 may travel in a direction the energy path P₂.

Absorbing energy by the vehicle 100 components 200 typically results ina mechanical stress in the respective components 200. Typically, astress applied to a component 200 is distributed non-uniformly, e.g.,concentrated in stress locations 210, as shown in FIGS. 2A-2B. In thepresent context, a stress concentration location 210 is a volume of acomponent 200 at which a far field stress magnitude applied by theloading energy path to the component 200 is magnified. In one example,an internal stress concentration location 210 may have a spherical, anellipsoidal, or any other suitable shape. As another example, anexternal stress concentration location 210 may have a shape of a notchor other abrupt change in the external surface of a component 200. Astress concentration location 210 (or stress location 210) in acomponent 200 may be based on a component 200 shape and constituentmaterials and/or a component 200 loading mode. Thus, in one example, ashape, e.g., dimensions of a stress concentration location 210, e.g., adiameter of a sphere surrounding a stress location 210, may bedetermined based on the shape and constituent materials and/or loadingmodes of the component using, e.g., finite element analysis (FEA), ahandbook of stress concentration factors, etc. Location coordinatesand/or dimensions of a stress location 210 may be specified based on acoordinate system, e.g., a Cartesian coordinate system with an origin ata vehicle 100 reference point 150. For example, as shown in FIGS. 2A-2B,the stress location(s) 210 of the component 200 may differ based on theloading mode. For example, the energy path P₁ of a frontal impact maycause multiple stress locations 210 (see FIG. 2A), whereas the energypath P₂ of a roof impact may cause a stress location 200 partiallyoverlapping or different from the stress locations 210 of the frontalimpact. The stress locations 210 may be identified using computer modelsof the component 200 and/or vehicle 100, as discussed with reference toFIG. 6.

As discussed above, a component 200 may be formed of composite materialsuch as a CFRP. When a composite material is loaded with a force, theforce generates stress, e.g., in stress locations 210, as shown in FIGS.2A-2B, which then may cause material damage 300. A force may be appliedto the material because of (i) an impact of another object to thevehicle 100, e.g., a crash, (ii) the vehicle 100 operation such asacceleration, deceleration, driving over pothole, etc., (iii) aging ofthe component 200, (iv) thermal and/or mechanical cycling, (v) varyingloading rate, and/or other sources of mechanical force. A stress to acomponent 200 may cause a damage 300 to the component. A damage 300, inthe present context, is an irreversible physical change in a CFRPcomponent 200. “Irreversible” means that the component 200 cannot returnto its original physical structure after removal of the applied stress.For example, fracture or yielding of the material is an irreversiblechange, whereas elastic deformation, expansion or contraction of thematerial, based on an ambient temperature change or mechanical load is areversible change. FIGS. 3A-3B illustrates multiple types of damage 300that can occur in a composite material such as CFRP. FIG. 3A showsexample damage 300 types including debonding, fiber rupture, matrixrupture, pull out, and bridging. FIG. 3B illustrate delamination asanother type of damage 300.

In the present context, an extent of damage 300 with respect to anentire component 200 is measured with a “component damage quantifier D”,whereas a local damage quantifier d specifies an extent of a damage 300at a specified location of a component 200, e.g., at a stress location210 of the component 200. The local damage quantifier d and componentdamage quantifier D may be specified in unitless numerical values, e.g.,a number between 0 (zero), i.e., no damage, and 1, i.e., damagedmaterial has no load carrying capability due to the damage (as discussedwith respect to FIGS. 6A-6B). Thus, a local damage quantifier d of acomponent 200 may have different values based on a location within thecomponent 200. For example, different local damage quantifier d valuesmay be measured in different stress locations 210 in an examplecomponent 200. A local damage quantifier d may be determined based ondata received from damage detection sensor(s) 130, e.g., a transducer,an ultrasonic sensor, etc., included in a component 200. A componentdamage quantifier D may be determined based on local damagequantifier(s) d of the component 200, environmental data, predictedfuture loading cycles, neural network models, etc. As discussed withreference to FIG. 7, the computer 110 may be programmed to determine thelocal damage quantifiers d and component 200 damage quantifier D.

FIG. 4 shows multiple graphs 410, 420, 430 of strain response of acomponent 200. In the present context and as is conventionallyunderstood, a “strain” is a geometric response of a component, system,material, etc. to an applied stress, e.g., the force caused in a loadingmode such as shown in FIGS. 2A-2B. The stress may be measured in unitsof MPa (Megapascal). In one example, the strain can be defined as anamount of a deformation, expansion, contraction, or shear in a 3D stressstate, etc., in a direction of an applied force divided by an initiallength of the material, e.g., component 200. CFRP components may exhibitanisotropic or isotropic elastic properties. An anisotropic response ofthe material may include different effective stiffness as a function ofapplied loading and coupling between in and out of plane strains. Inaddition, the yielding, damage initiation, and damage evolution of aCFRP component 200 may exhibit anisotropic or isotropic response. Forexample, a homogenous polymer composite with chopped fibers of randomdirections would be exhibited to exhibit an isotropic response whereas alayup of multiple CFRP layers with unidirectional long fibers at varyingangles in each layup would be expected to be anisotropic in response.The strain value may be unitless and may be measured as a change ofmaterial length in units of original material length, e.g., in PPM (partper million), μm/m (micrometer per meter), etc. As shown in the examplegraphs 410, 420, 430, an increased amount of “local damage quantifier d”may cause a “strain” in the component 200 upon applying an even lowerstress to the component 200. In other words, a component 200 with ahigher damage quantifier d is more susceptible to failure, yielding,lower energy absorption, lower structural rigidity, etc.

To achieve an expected performance, e.g., with respect to safetystandards such as small overlap rigid barrier (SORB), the component 200may be designed to withstand a specified amount of stress, e.g., st₁ asshown in FIG. 4. In the present context, to “withstand” means to preventa strain that exceeds a predetermined threshold n₁ unless the stressexceeds the threshold st₁. The example graphs 410, 420, 430 illustratesthat a component 200 may fail to withstand the stress when a localdamage quantifier d increases. For example, as shown in the examplegraph 430, the component 200 fails to withstand the stress when thedamage quantifier d is equal 0.8. A failing component 200 may impact avehicle 100 operation, e.g., not satisfying a safety standard.

With reference to FIGS. 5A-5B, the computer 110 may be programmed todetermine a damage quantifier D for a vehicle 100 component 200 formedof composite material based on vehicle 100 sensor 130 data, and tooperate the vehicle 100 based on a mission determined according todetermined damage quantifier D. In the present context, a componentdamage quantifier D specifies a level of damage 300 associated with acomponent 200 based on determined local damage quantifiers d in variousareas, e.g., stress locations 210, of the component 200 in form of anumerical value, e.g., in a range of 0 (zero), i.e., no damage, to 1,i.e., the component has no load carrying capability due to the damage,as discussed with respect to FIG. 7.

As discussed above with reference to FIGS. 2A-2B, a component 200 mayhave different stress locations 210 based on a loading mode, i.e., amagnitude and direction of a force vector at the location 210, e.g.,front impact, roof impact, etc. With respect to FIGS. 5A-5B, the stresslocations 210 shown include stress locations 210 of multiple loadingmodes, e.g., stress locations of both FIGS. 2A-2B. The computer 110 maybe programmed to receive data associated with multiple loading modes ofthe component 200. FIG. 5A shows an example placement of sensors 130,e.g., substantially uniformly distributed in the composite material ofthe component 200, whereas FIG. 5B shows an example of specificallyselected number and/or location of sensors 130 based on location ofstress locations 210 of multiple loading modes in the respectivecomponent 200, as discussed below with respect to a process 1100illustrated in FIG. 11.

With continued reference to FIGS. 5A-5B, the computer 110 may beprogrammed to receive data, e.g., electrical signals, packets, etc.,from the sensors 130 and to determine the local damage quantifier dbased on the received data. For example, the sensors 130 included in thecomponent 200 may include a piezoelectric, a transducer, a capacitivesensor, a strain sensor, microelectromechanical system (MEMS), and/or amagnetostrictive device. In one example, illustrated in Table 1, anultrasonic damage detection sensor 130 may include a transmitter and areceiver of ultrasonic signals.

TABLE 1 Received ultrasonic signal intensity Estimated local damagequantifier d 90% 0.1 80% 0.2 . . . 20% 0.8  0% 1.0

In the example shown in Table 1, a sensor 130 transmitter and receivermay be mounted in a stress location 210. For example, the transmitterand receiver may be mounted on opposing sides of a spherical-shapedstress location 210, i.e., two ends connected by an imaginary linepassing through a center of the spherical shaped stress location 210.The computer 110 may be programmed to actuate the transmitter totransmit an ultrasonic signal. A damage 300 in the stress location 210,in which the sensor 130 is located, may, e.g., reduce an intensity,change a shape, etc., of a received ultrasonic signal at the sensor 130receiver. The computer 110 may be programmed to determine an intensity,shape, etc., of the ultrasonic signal received at the sensor 130receiver. The computer 110 may be programmed to store data including anexpected signal shape, intensity, etc. of the received ultrasonic signalbased on non-damaged and damaged material, and to determine the localdamage quantifier d based on the stored data and the received ultrasonicsignal. For example, with reference to Table 1, the computer 110 may beprogrammed to store a table including a relationship of (i) the localdamage quantifier d and (ii) a percentage of received signal intensitycompared to an expected signal intensity associated with a non-damagedmaterial. Additionally or alternatively, the computer 110 may beprogrammed to determine a local damage quantifier d using any othersuitable relationship, e.g., exponential, square, etc., of sensor 130data and the local damage quantifier d. Additionally or alternatively,the computer 110 may be programmed to determine a local damagequantifier d based on a neural network that is trained based on groundtruth data, as discussed with respect to FIG. 6.

With reference to FIG. 5B, a number and/or location of sensors 130 maybe identified as a part of a design of the component 200 and/or thevehicle 100 body 160. For example, a lab computer may be programmed toidentify the number and/or locations of the sensors 130 on, within, oradjacent (e.g., touching) a component 200 to monitor local damagequantifier(s) d based on a Failure Mode Effect Analysis (FMEA)technique, a computer aided engineering simulation such as a FiniteElement Analysis (FEA), Computational Fluid Dynamics (CFD), and/or othercomputational techniques. In one example, the lab computer may beprogrammed to identify location and number of damage detection sensors130 by minimization of a covariance of estimated parameters, Guyan modelreduction, effective independence, kinetic energy, neural networks,genetic algorithm, simulated annealing, etc. Additionally oralternatively, the lab computer may be programmed to identify locationsof damage detection sensors 130 based on a sparse array sensoroptimization technique

The lab computer maybe programmed to identify stress locations 210 inthe vehicle 100 components 200 for one or more loading modes, e.g., aroof impact, a frontal impact, a rear impact, and a side impact (seeFIGS. 2A-2B). Further, the lab computer may be programmed to identifysensor 130 locations in the vehicle 100 component 200 based on theidentified stress locations 210. The sensors 130 may be then placed ormounted at the identified sensor 130 locations. In the present context,“at” the identified location includes in, on, proximate to (i.e., withina predetermined distance from, e.g., 1 centimeter), and/or touching thecomponent 200.

To program the vehicle 100 computer 110 to determine a component damagequantifier D and/or a local damage quantifier d, the lab computer may beprogrammed, using FEA, FMEA, etc., to generate training data (e.g.,Table 2). As discussed below, a machine learning algorithm such asneural network may be trained based on the generated training data, andthe vehicle 100 computer 110 may be programmed based on the trainedneural network to determine the local damage quantifiers d, and/orcomponent damage quantifier D.

TABLE 2 Damage data Location, dimensions, type of damage (e.g., rupture,debonding, etc.) Loading mode Front crash including impact force data.Sensor characteristics data Data describing relationship of physicalattributes of material to sensor data Sensor location data Dataspecifying location of damage detection sensors in a component SensorData Data received from damage detection sensors associated with thedamage in the component First local damage 0.3 quantifier Second localdamage 0.7 quantifier SORB safety rating Marginal NVH safety ratingAcceptable Component damage 0.6 quantifier D

In the present context, training data, i.e., values specifying physicalphenomena under baselines or various conditions. Training data typicallyincludes input data describing an example damage 300, expected data fromsensors 130 associated with the damage 300, and data describing howattributes such component damage quantifier D, etc. are estimated undersuch conditions (i.e., simulated damage 300). With reference to exampleTable 2, training data may include input data including (i) damage 300data, e.g., dimensions of a damage 300, e.g., a crack, type of damage300, e.g., rupture, etc., (ii) loading mode(s) applied to the damagedcomponent 200, e.g., an amount and/or direction of force applied to thedamaged component 200, (iii) sensor 130 characteristic data, e.g., datawhich specify a relationship of a physical attribute of the material tothe received sensor 130 data and data specifying materialcharacteristics, and (iv) sensor 130 location data specifying locationcoordinates of the sensors 130 with respect to a coordinate system,e.g., a Cartesian coordinate system with an origin at the referencepoint 150. Additionally or alternatively, the location coordinates ofthe sensors 130 may be specified with respect to other types ofcoordinate systems such as cylindrical, spherical, Curvilinearcoordinates, Plücker coordinates, Canonical coordinates, etc.

The lab computer may be programmed to generate, based on the input datadescribed above, the training output data including (i) sensor 130 data,e.g., signal data expected from the sensors 130 based on the sensor 130locations, sensor 130 characteristics, damage 300 data, etc., (ii)damage quantifier(s) d based on damage 300 data, e.g., dimensions ofdamage 300 such as depth of the damage 300 (and, e.g., an example Table1 specifying a relationship of data received from a sensor 130 and alocal damage quantifier d), (iii) safety ratings, as discussed below,and (iv) the component damage quantifier D based on the damagequantifiers d, safety ratings, etc., as discussed below.

The lab computer may be programmed to estimate the signal that may bereceived from the sensors 130 based on the damage 300 to the compositematerial, e.g., based on known dimensions, etc. The lab computer may beprogrammed to generate the ground truth data based on a known locationof the damage 300, simulating effects of the damage 300 on physicalattributes of the material and based on sensor characteristics datawhich specify a relationship of a physical attribute of the material tothe received sensor 130 data. In other words, the generated trainingdata may include examples of how the received sensor 130 data changebased on damages caused in various locations, levels of severity, andpotentially types of damage (e.g. a specific type of damage mode of theCFRP) of the component 200. Additionally or alternatively, the labcomputer may be programmed to generate the ground truth data based onmeasurement data received from lab measurement equipment such as X-Ray,ultrasound scanner, etc.

In the present context, a “safety rating” is a quantifier determininghow safe is an operation of the respective component 200 of the vehicle100. A vehicle 100 safety rating may be measured in a unitless scale,e.g., good, acceptable, marginal, poor, and/or a range, e.g., from 1(good) to 5 (poor). A safety rating may be determined based on varioussafety standards such as SORB, and/or any other safety standard providedby EURO NCAP, NHTSA, etc. Additionally or alternatively, a “safetyrating” may be determined based on other vehicle 100 attributes such asvehicle 100 ride handling/quality, NVH (Noise Vibration Harshness),aerodynamic properties, etc. The lab computer may be programmed todetermine the safety rating based on FEA techniques (e.g., based onestimating displacement, stress, acceleration, damage initiation,yielding dummy injury metrics, etc.) and to determine the safety ratingof the component 200 based on the determined local damage quantifier(s)d, damage 300 data, etc.

The lab computer may be programmed to determine the safety rating of thecomponent 200 based on the damage 300 data, component 200 data, e.g.,dimensions, material, etc., vehicle 100 design (shape, dimensions,mechanical properties, etc.), loading mode, etc. In other words, basedon known damage 300 to the component 200, the lab computer 110 may beprogrammed to simulate (or estimate) how far a safety rating iscompromised, and further determine the safety rating based on knownsafety standards, e.g., SORB, etc.

In one example shown in FIG. 6, an example graph 610 shows arelationship of the component 200 safety rating and the component damagequantifier D. The lab computer may be programmed to determine thecomponent damage quantifier D, based at least in part on the safetyrating. Additionally or alternatively, the lab computer may beprogrammed to store any other relationship of safety ratings and thecomponent damage quantifier D. Additionally or alternatively, asdiscussed below with reference to FIG. 10, the safety rating may bedetermined for a vehicle 100 rather than a vehicle 100 component 200.

The generated training data may be used to train a machine learningalgorithm for determining component damage quantifier D, a vehicledamage quantifier D, etc. A “neural network” (NN) is a computing systemimplemented in software and/or hardware that is inspired by biologicalneural networks that constitute brain operation. A neural network learnsto perform tasks by studying examples generally without being programmedwith any task-specific rules. A neural network can be a software programthat can be loaded in memory and executed by a processor included in acomputer, for example the computer 110. The neural network can include ninput nodes, each accepting a set of inputs i (i.e., each set of inputsi can include on or more inputs x). The neural network can include moutput nodes (where m and n may be, but typically are not, a samenumber) provide sets of outputs o₁ . . . o_(m). A neural networktypically includes a plurality of layers, including a number of hiddenlayers, each layer including one or more nodes. The nodes are sometimesreferred to as artificial neurons, because they are designed to emulatebiological, e.g., human, neurons. For example, a neural network maylearn to determine a damage 300 in a component 200 by analyzing trainingdata generated based on FEA techniques and/or real-world examples, e.g.,measurement of sensor 130 data in components with and without damages inthe composite material. For example, the neural network may learn todetermine a component damage quantifier D based on local damagequantifiers d determined based on received sensor 130 data and knownground truth about location, dimension, etc., of damage(s) 300 in therespective component 200. Further, training of the neural network maycontinue after deployment of the component 200 and/or vehicle 100, e.g.,to learn how the damages evolve in a vehicle 100 component 200 based oneffects of environmental conditions, aging, etc., and/or improvedetection of damages (see FIG. 13). Additionally or alternatively, othermachine learning techniques, such as SVM (Support Vector Machine),decision trees, naïve-bayes, ensemble methods, etc. may be used toidentify a relationship of damages in the component 200 and datareceived from the damage detection sensors 130.

The vehicle 100 computer 110 may be programmed (e.g., based on thetraining data discussed above) to determine the local damage quantifierd of a component 200 based on the data received from the sensor 130included in the vehicle 100 component 200, and to determine thecomponent damage quantifier D based on the determined local damagequantifier(s) d, location of the sensors 130, and/or distances of thesensors 130 to one another, etc. Herein below multiple examples fordetermining a component damage quantifier D are discussed.

In one example, the computer 110 may be programmed to determine thecomponent damage quantifier D to be a maximum of local damagequantifiers d of the component 200. For example, upon determining thelocal damage quantifiers d (e.g., as discussed above regarding exampleTable 1) including 0.2, 0.3, 0.5, the computer 110 may determine thecomponent damage quantifier D to be equal 0.5.

In another example, the computer 110 may be programmed to determine thecomponent damage quantifier D based on machine learning, e.g., in aneural network which takes the local damage quantifier(s) d as input,then outputs the damage quantifier D for the respective vehicle 100component 200. The neural network may be trained, based on the groundtruth data, to determine the component damage quantifier D based on thereceived sensor 130 data. As discussed above, multiple sensors 130 maybe included in a component 200. Thus, the computer 110 may be programmedbased on a machine learning technique to receive data from multiplesensors 130 and to determine the damage quantifier D of the component200 based on the received sensor 130 data.

Additionally or alternatively, the computer 110 may be programmed todetermine the component damage quantifier D based on a mechanical modelof the component 200. Thus, the computer 110 may be programmed todetermine the damage quantifier D based on the determined local damagequantifiers d and the mechanical model, e.g., by utilizing FEAtechniques. A component 200 may have a complex geometric and/ormechanical model, e.g., a complex geometry resulting in a fine meshmodel using complex materials models to predict local/or globalcomponent response. To reduce computation time, the computer 110 may beprogrammed to determine the component damage quantifier 200 based on asimplified (or abstract) mechanical model of the component 200. In thepresent context, a simplified mechanical model is a model thatsubstantially includes physical properties of the component 200 althoughinsignificant details are removed, e.g., a straight beam model may beused as a simplified model of a body 160 pillar. Additionally oralternatively, as discussed with respect to FIG. 11, the computer 110may be programmed to determine a vehicle 100 damage quantifierD_(vehicle) i.e., a damage quantifier that specifies an extent of damagefor the vehicle 100.

FIG. 8 shows examples graphs 800, 810 which illustrates applied stress,e.g., an amount of pressure measured in mega pascal (MPa) and a numberof cycles to failure (i.e., a number of operation cycles until anexample component 200 and/or a system fails to operate). In the presentcontext, “failure” or “fails” means having a safety rating below athreshold, e.g., having “poor” safety rating based on SORB. The examplegraphs 800, 810 show two different environmental conditions(temperature) applied; the graph 800 shows the operation at a firsttemperature 298 degrees Kelvin (K), whereas the graph 810 shows theoperations at a second temperature 373 degrees K.

As shown in the example graphs 800, 810, harsher environmentalconditions such as increased temperature with respect to graph 810 incomparison to the graph 900, may result in a failure of a component 200under lower stress levels. Additionally or alternatively, otherenvironmental conditions such as humidity, air pressure, etc. may affecta number of operation cycles until a component 200 and/or a system,e.g., a vehicle 100 fails to operate. Additionally or alternatively,vehicle 100 physical attributes such as speed, acceleration, and/or roadconditions, e.g., potholes, etc., may affect a number of operationcycles of a component 200 and/or vehicle 100 fails to operate. In thepresent context, an environmental parameter (or condition) include (i)ambient-related parameters such as temperature, pressure, humidity,etc., (ii) vehicle 100 physical attributes such as speed, acceleration,etc., and/or (iii) road conditions, e.g., potholes, roughness (dirt roadversus paved road), etc.

With continued reference to FIG. 8, a change of an environmentalcondition may change a number of operation cycles until a component 200and/o a system fails. A reduced number of operation cycles until failuremay result in an increased damage quantifier D. In other words, a higherdamage quantifier D means that a likelihood of an earlier failure of therespective component 200 and/or system.

As discussed above with reference to Table 2, a damage quantifier D maybe related to a safety rating. For example, a component 200 with adamage 300 may have a higher likelihood of failure with respect to SORBstandard whereas a lower likelihood of failure with respect to anothersafety standard, e.g., NVH. For example, with respect to FIG. 9, a firstexample graph 910 shows a change of a component 200 damage quantifier Dwith respect to a first standard, e.g., NVH, whereas a second examplegraph 920 shows a change of the component 200 damage quantifier D withrespect to a second standard, e.g., SORB. The computer 110 may beprogrammed to predict a change of a damage quantifier D based on otherdata such as environmental conditions, cycles of use, etc., based on acombination of multiple safety standards, e.g., SORB and NVH.

In one example, the computer 110 may be programmed to determine amaximum value of damage quantifier D of multiple quantifiers Ddetermined based on different safety ratings. For example, the examplegraph 930 shows a change of damage quantifier D based on theenvironmental parameter which is a maximum of damage quantifiers Ddetermined based on the first and second safety standards, e.g., SORB,NVH. Additionally or alternatively, the computer 110 may be programmedto determine the damage quantifier D based on multiple safety standards,environmental conditions, etc. using various machine learningtechniques, e.g., neural networks.

As discussed above, the computer 110 may be programmed to select amission for the vehicle 100 based on the damage quantifier D and tonavigate the vehicle 100 based on the selected mission. In the presentcontext, a “mission” includes a mode of navigation including at leastone of cargo-only, cargo and passenger, move with no cargo or passenger,stop movement, etc. For example, as shown in example Table 3, thecomputer 110 may be programmed to select a mode of navigation based onthe component damage quantifier D and multiple damage quantifierthresholds Th₁, Th₂, Th₃, e.g., 0.2, 0.4, 0.6. The computer 110 may beprogrammed to actuate a vehicle 100 propulsion and/or brake actuator 120to stop the vehicle upon selecting the “stop movement” mission.

TABLE 3 Mode of navigation Condition for mode of navigation Cargo andpassenger D ≤ 0.2 Cargo-only 0.2 < D < 0.4 or D = 0.4 No cargo orpassenger 0.4 < D < 0.6 Stop movement D ≥ 0.6

Additionally or alternatively, the mission may include a routinglimitation(s), e.g., no freeway, and/or physical attributeslimitation(s), e.g., maximum speed, etc. For example, the computer 110may be programmed to select a “no freeway” mission upon determining thatthe damage quantifier D exceeds a threshold, e.g., 0.5. Thus, thecomputer 110 may be programmed to plan a route using conventionalrouting techniques, which lack any segment of the route to be on afreeway. Additionally or alternatively, the computer 110 may beprogrammed to limit the vehicle 100 speed by actuating the vehicle 100propulsion based on a selected maximum speed, e.g., 80 kilometer/hour(kph), upon determining that the damage quantifier D exceeds athreshold, e.g., 0.5.

FIG. 10 shows damages 300 in multiple components 200 of the vehicle 100body 160. For example, the components 200 may include one or more damagedetection sensors 130 to detect damages 300 in the respective component200. The computer 110 may be programmed to determine a component damagequantifier D (or D_(part)) for the components 200. The computer 110 maybe programmed to determine when an individual component 200 needs to berepaired or replaced based on the damage quantifier D_(part) of therespective component 200. For example, the computer may be programmed toactuate the vehicle 100 to navigate to a service center for replacing acomponent 200 upon determining that the damage quantifier D_(part) ofthe respective component exceeds a predetermined threshold, e.g., 0.5.Additionally, the computer 110 may be programmed to determine a vehicledamage quantifier D_(vehicle) (or a damage quantifier D_(vehicle) forthe vehicle) based at least in part on the determined component damagequantifiers D_(part). In one example, the computer 110 may be programmedto determine the vehicle damage quantifier D_(vehicle) to be a maximumof determined components damage quantifier(s) d.

The computer 110 may be programmed to determine the vehicle damagequantifier D_(vehicle) based on a combination of the component damagequantifiers D, local damage quantifiers d of the components 200,environmental conditions, a mechanical model of the vehicle 100 based ona machine learning technique. For example, a neural network may betrained based on the ground truth data, e.g., see Table 2, environmentalconditions, and mechanical model of the vehicle 100.

As already discussed above, the computer 110 may be programmed todetermine component damage quantifier D and/or vehicle damage quantifierD_(vehicle) based on current (or present) data from damage detectionsensors 130, other vehicle 100 sensors such as temperature, humidity,etc. sensors 130, etc. In addition, the computer 110 may be programmedto predict a change of damage quantifier D of a component 200 and/or thevehicle 100 based on received predictions of vehicle 100 route, expectedcycles of use, weather conditions, and/or loading of the vehicle 100(i.e., number of passenger, weight of cargo, etc.). For example, withrespect to FIG. 10, the computer 110 may be programmed to predict achange of the damage quantifier D based on a predicted environmentalcondition, e.g., received temperature and/or humidity forecast data. Asanother example, the computer 110 may predict a change of the damagequantifier D based on a planned route, e.g., based on received dataindicating a rough road as a part of the planned route and determineddamage 300 in a suspension component 200 of the vehicle 100. In oneexample implementation, a prediction may be implemented using arule-based technique. An example rule may include to predict anincrease, e.g., 20%, of the damage quantifier D of any suspensioncomponent 200 upon determining that a planned route includes a roughsurface, e.g., determined based on received map data. Such rules may begenerated based on training a machine learning system.

In another example, an FEA model may include relationships ofenvironmental conditions such as temperature, humidity, predicted weightof loads and/or passengers, etc. and component 200 mechanical responses,e.g., elastic deformation, inelastic deformation, fatigue degradationresponse, environmental interaction, degradation, energy absorption,etc. A lab computer may be programmed to simulate an FEA model undervarious conditions (i.e., various environmental conditions, loads,impacts, damages, etc.) and to generate simulation results. A machinelearning model such as a neural network may be trained based on thesimulation results (and/or real-world measurements as discussed below)to take the sensor 130 data, environmental data, planned route, etc. andto output predicted vehicle and/or component damage quantifier D.

In addition to predicting a change of damage quantifier D based onenvironmental conditions, planned route, etc., the computer 110 may beprogrammed to determine progress of the damage 300 over time and adjusta prediction of the damaged quantifier D. In the present context,“progress of damage” is an expansion, widening, and/or increasedseverity of a damage 300. For example, a widening of a crack damage 300is a progress of damage 300. The computer 110 may determine a firstdamage quantifier D of a component 200 and a time t₁, and predict asecond damage quantifier D at a time t₂ based on planned route, forecastof environmental conditions, etc. At the time t₂, the computer 110 maydetermine an actual damage quantifier D and may determine the progressof the damage based at least in part on the predicted second damagequantifier D and the actual determined damage quantifier at the time t₂.Thus, the computer 110 may be programmed to adjust a prediction of athird damage quantifier at a time t₃ based on the determined progress ofthe damage, e.g., based on a progressive damage modelling technique suchas Hashin type criteria, Tsai Hill criteria, etc.

FIG. 11 shows a flowchart of a process 1100 for identifying a number ofand/or locations of the sensors 130 in a component 200. A lab computeror the like, i.e., a general-purpose computer such as may be used in atesting facility, may be programmed to execute blocks of the process1100.

The process begins in a block 1110, in which the lab computer receivescomponent 200 data. The lab computer may be programmed to receiveComputer Aided Design (CAD) data specifying dimensions, shape, material,etc. of one or more components 200. The lab computer may be programmedto receive a FMEA, FEA model, etc., of the component 200 and/or thevehicle 100. The FMEA may include identifying failure modes of thecomponent 200, and their causes and effects. In the context of a FMEAtechnique, “Failure modes” means ways, or modes, in which something mayfail, i./e., cease to be usable for its intended function or operation.For example, crack in a pillar component 200 of the vehicle 100 may bedescribed as one or more failure modes. Further, the lab computer may beprogrammed to receive material specification data, e.g., materialelasticity, toughness, etc. The lab computer may be programmed toidentify stress locations 210 based at least in part on FMEA data, e.g.,location in which FMEA data indicate one or more failure modes.

Next, in a block 1120, the lab computer receives sensor 130specification data. Sensor specification data, in the present context,means data describing a relationship of a physical attribute, e.g.,conductance of ultrasound signal, capacitance, conductance of magneticsignals, etc., to a physical and/or electrical parameter, e.g., voltageamplitude, frequency, etc., of a signal received from the damagedetection sensor 130.

Next, in a block 1130, the lab computer determines component 200 loadingmodes and stress locations 210 based on the received component designdata, FMEA data, material specification data, etc. For example,utilizing an FEA technique, the lab computer may be programmed todetermine stress locations 210 of the component 200 by simulatingvarious loading modes, e.g., front crash, side crash, etc. of thevehicle 100.

Next, in a block 1140, the lab computer determines location(s) ofrespective damage detection sensor(s) 130 in the component 200. The labcomputer may be programmed to determine the number and/or locations ofthe sensors 130 based on the identified stress locations 210 and/oridentified loading modes. In one example, the lab computer may beprogrammed to determine the locations of the sensors 130 such that atleast one sensor 130 is located at each of the identified stresslocations 210, e.g., including a transmitter and a receiver (animaginary line connecting the transmitter and the receiver may intersectthe respective stress location 210). In one example, the lab computermay be programmed to determine locations of sensors 130 based on each ofthe loading modes, e.g., front impact loading mode and roof crashloading mode (see FIGS. 2A-2B). In another example, the lab computer maybe programmed to select one or more types of loading mode for detection,and to identify the sensor 130 locations based on the selected types ofloading mode. For example, the lab computer may be programmed to selecta frontal impact loading mode (e.g., the example loading mode shown inFIG. 2A), and to identify the damage detection sensor 130 locationsbased on the selected loading mode. Additionally or alternatively, thelab computer may be programmed to determine location of sensors 130based on the received sensor characteristics data. For example, the labcomputer may determine a first location for a transducer sensor 130whereas a second location for an ultrasonic sensor 130. In other words,a location of sensor 130 may be based on characteristics of the sensor130, e.g., range of detection, precision, etc.

Next, in a block 1150, the lab computer may be programmed, using FEA,FMEA, etc., to generate training data, e.g., example Table 2, based onsimulating damages 300 applied to the component 200. Additionally oralternatively, the lab computer may be programmed to receivemeasurements data from a lab measurement equipment, e.g., x-ray,ultrasound scanner, etc. The received data may include data collected ata time of inspecting a vehicle 100 in a service center.

Next, in a block 1160, the lab computer 110 trains an artificial (ornon-biological) neural network based on the generated training data.Following the block 1160, the process 1100 ends, or alternativelyreturns to the block 1110, although not shown in FIG. 11.

FIG. 12 is a flowchart of an example process 1200 for performing vehicle100 operation. A vehicle 100 computer 110 may be programmed to executeblocks of the process 1200.

The process 1200 begins in a block 1210, in which the computer 110receives neural network data. In one example, the computer 110 may beprogrammed to receive neural network data during a manufacturing step ofprogramming the computer 110. Additionally, the computer 110 may beprogrammed to receive updated neural network (or re-trained neuralnetwork data) from a remote computer via a wireless network, e.g., in aperiodic manner and/or upon an availability of an updated neural networkon the remote computer.

Next, in a block 1220, the computer 110 receives vehicle 100 sensor 130data. The computer 110 may be programmed to receive data from damagedetection sensors 130 included in the vehicle 100 body 160 (e.g., inmultiple components 200).

Next, in a block 1230, the computer 110 receives environmental data. Thecomputer 110 may be programmed to receive environmental data fromvehicle 100 sensors 130 such as temperature, pressure, speed,acceleration, etc. sensors 130. The computer 110 may be programmed toreceive, from a remote computer, environmental data such as predictedweather data, road data, etc. Additionally, the computer 110 may beprogrammed to receive planned route data.

Next, in a block 1240, the computer 110 determines local damagequantifiers d. The computer 110 may be programmed to determine the localdamage quantifiers d for each location in which a damage detectionsensor 130 is placed, based on the received sensor 130 data. Thus, thecomputer 110 may be programmed to determine one or more local damagequantifiers d for each of the components 200 based on a specifiedrelationship between received sensor 130 data and the local damagequantifier d, an example being illustrated in Table 1 above.

Next, in a block 1250, the computer 110 determines component and/orvehicle damage quantifiers D. The computer 110 may be programmed todetermine the damage quantifiers D based on received damage detectionsensor 130 data, environmental data, and the received neural networkmodel. Additionally, the computer 110 may be programmed to predict achange of the damage quantifier D based on the predicted environmentaldata, planned route data, etc. Additionally, the computer 110 may beprogrammed to predict an adjusted progression of a previously detecteddamage 300 based on received sensor 130 data and a comparison ofpreviously predicted change of damage quantifiers D and a currentlydetermined damaged quantifier D.

Next, in a block 1260, the computer 110 selects a mission for thevehicle 100 based on the determined component and/or vehicle damagequantifier D. In one example, the computer 110 may select a mission fromone of modes of navigation including cargo-only, cargo and passenger,move with no cargo or passenger, and/or stop movement. In anotherexample, the computer 110 may select a mission including one of a speedlimitation (i.e., determining a maximum allowed vehicle 100 speed), roadtype limitation (e.g., no freeway), etc.

Next, in a block 1270, the computer 110 operates the vehicle 100 basedon the selected mission. In one example, the computer 110 may beprogrammed to select a mission, e.g., a mode of navigation, asillustrated regarding example Table 3 above. The computer 110 may beprogrammed to actuate a vehicle 100 propulsion and/or brake actuator 120to stop the vehicle upon selecting the “stop movement” mission.Additionally or alternatively, upon selecting the mission “move no cargoor passenger”, the computer 110 may be programmed to navigate thevehicle 100 to a nearest service center for repair if the vehicle 100 ismovable; otherwise, the computer 110 may be programmed to stop thevehicle 100 or navigate the vehicle at least to a road side to preventroad blockage.

In another example, the computer 110 may be programmed to operate thevehicle 100 with a reduced speed upon determining a mission including amaximum allowed speed. In yet another example, the computer 110 may beprogrammed to reroute the vehicle 100, e.g., upon determining a missionincluding a limitation of “no freeway.” Thus, the computer 110 may beprogrammed to plan a second route that lacks any freeway. Following theblock 1270, the process 1200 ends, or returns to the block 1210,although not shown in FIG. 12.

As discussed above, a neural network may be trained based on FEAresults, FMEA, etc. In one example, the neural network may be trainedbased on failure modes included in the FMEA data. Additionally, theneural network may be trained based on ex situ measurements while thevehicle may be stationary, e.g., scanning or inspection tools or thelike that can include ultrasonic, x-ray inspection, vibrography, and/orother techniques that scan a surface of vehicle 100 body 160 to detectdamages 300. In the present context, “ex situ” measurement is ameasurement performed by a device that is not included in the vehicle100, e.g., in a lab, a service center inspection equipment, vehicle 100depot, etc. Such methods provide a more direct detection and imaging ofdamage 300 in CFRP components 200. A lab computer may be programmed togenerate training data for the neural network. The training data mayinclude ground truth data such as shown in the example provided in Table4.

TABLE 4 Data item(s) Description Scan data Data including type ofdamages, location of damages, dimension, severity, etc. of damages.Local damage Determined by the vehicle computer based on quantifier(s) dvehicle sensor data and pre-stored programming of for each of vehiclecomputer detected damages Component and/or Determined by the vehiclecomputer based on vehicle damage pre-stored neural network model,programming, quantifier D etc.

During inspection and maintenance of a vehicle 100, a damage 300identified measurements from scanning tools or the like, may be used toimprove damage detection techniques and to predict the component damagequantifier D based on environmental conditions, sensor 130 data, etc.Typically, scanning tools or the like provide higher precision datacompared to vehicle 100 sensors 130. Such higher precision datacollected from a scanning or inspection tool can be used to re-train theneural network.

For example, the neural network may be re-trained based on collectedre-training data, e.g., as illustrated in Table 4. In one example,“re-training” may include updating weights of network nodes (of astandard neural network, a Bayesian neural network, etc.) using gradientdescent to minimize a loss (error) of the network. The re-training datacollected based on scanning tool data (herein referred to as re-trainingdata because the neural network has been originally trained beforedeployment in the vehicle 100 computers) may be stored in a labcomputer. The lab computer may be programmed to re-train the neuralnetwork based on the re-training data received, e.g., via a wide areacommunication network from a service center computer. The lab computermay be programmed to update vehicle 100 computers 110 based on there-trained neural network. In one example, the remote computer 110, viaa wired and/or wireless network communication, may overwrite an existingprogramming of multiple computers 110 (e.g., computers 110 in a fleet ofvehicles 100) based on the re-trained neural network.

FIG. 13 shows a flowchart of an example process 1300 for re-training (orupdating) of a neural network that determines the damage quantifiers D.For example, a lab computer, i.e., a general-purpose computer in atesting, manufacturing, or fleet management facility, may be programmedto execute blocks of the process 1300.

The process 1300 begins in a block 1310, in which the remote computerreceives neural network data and/or any other machine learning algorithmdata for determining the damage quantifiers D, local damage quantifierd, and/or vehicle damage quantifier D_(vehicle). In one example, theremote computer may receive the neural network data from a remotecomputer memory. The neural network data include data that describes theneural network model.

Next, in a block 1320, the remote computer receives re-training data,e.g., from a service center computer. The remote computer may beprogrammed to receive re-training data including ground truth such asillustrated by Table 4. The received re-training data may includemeasurement data collected by scanning tools, etc. in the respectiveservice center and determined sensor 130 data, local damage quantifiersd, and/or component and/or vehicle damage quantifiers D received fromthe vehicle computer 110.

Next, in a block 1330, the remote computer re-trains the neural networkof the vehicle 100 computer 110. The remote computer may be programmedto identify the neural network of the vehicle 100 computer 110 and tore-train the neural network based on the received re-train data. In oneexample, most recent updates of neural network models may be stored on aremote computer memory and the remote computer may be programmed toidentify the neural network for the respective vehicle 100 type based ona vehicle 100 model number, etc., which may be included in the receiveddata from the service center computer.

Next, in a block 1340, the remote computer updates the vehicle 100computer 110 based on the re-trained neural network. For example, theremote computer 110 may be programmed to transmit, via a wireless and/orwired communication network, re-trained neural network data (e.g., usingBayesian Neural Network technique) to the vehicle 100 computer 110.Additionally, the remote computer may be programmed to update thecomputers 110 of a fleet of the vehicles 100 based on the re-trainedneural network.

Following the block 1340, the process 1300 ends, or alternativelyreturns to the block 1310, although not shown in FIG. 13.

Computing devices as discussed herein generally each includeinstructions executable by one or more computing devices such as thoseidentified above, and for carrying out blocks or steps of processesdescribed above. Computer-executable instructions may be compiled orinterpreted from computer programs created using a variety ofprogramming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Python, Java™, C, C++,Visual Basic, Java Script, Perl, HTML, etc. In general, a processor(e.g., a microprocessor) receives instructions, e.g., from a memory, acomputer-readable medium, etc., and executes these instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein. Such instructions and other data may be stored andtransmitted using a variety of computer-readable media. A file in thecomputing device is generally a collection of data stored on a computerreadable medium, such as a storage medium, a random-access memory, etc.

A computer-readable medium includes any medium that participates inproviding data (e.g., instructions), which may be read by a computer.Such a medium may take many forms, including, but not limited to,non-volatile media, volatile media, etc. Non-volatile media include, forexample, optical or magnetic disks and other persistent memory. Volatilemedia include dynamic random-access memory (DRAM), which typicallyconstitutes a main memory. Common forms of computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH, an EEPROM, anyother memory chip or cartridge, or any other medium from which acomputer can read.

With regard to the media, processes, systems, methods, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. In other words, the descriptions ofsystems and/or processes herein are provided for the purpose ofillustrating certain embodiments, and should in no way be construed soas to limit the disclosed subject matter.

Accordingly, it is to be understood that the present disclosure,including the above description and the accompanying figures and belowclaims, is intended to be illustrative and not restrictive. Manyembodiments and applications other than the examples provided would beapparent to those of skill in the art upon reading the abovedescription. The scope of the invention should be determined, not withreference to the above description, but should instead be determinedwith reference to claims appended hereto and/or included in anon-provisional patent application based hereon, along with the fullscope of equivalents to which such claims are entitled. It isanticipated and intended that future developments will occur in the artsdiscussed herein, and that the disclosed systems and methods will beincorporated into such future embodiments. In sum, it should beunderstood that the disclosed subject matter is capable of modificationand variation.

What is claimed is:
 1. A method, comprising: prior to deployment of avehicle, identifying stress locations in a first vehicle component forone or more types of loading modes of the first vehicle component, basedon inputting the loading modes and data describing the component to asimulation that outputs the stress location; wherein respective types ofloading modes specify directional forces to the first vehicle componentwhen a plurality of vehicle components receive one or more forces froman impact to the vehicle; identifying a plurality of sensor locations inthe vehicle component for the plurality of sensors based on theidentified stress locations in the vehicle component that specify thedirectional forces to the vehicle component; and mounting the pluralityof the sensors at the identified sensor locations.
 2. The method ofclaim 1, further comprising: upon deployment of the vehicle, determininga damage quantifier for a vehicle component formed of composite materialbased on vehicle sensor data; and operating the vehicle based on amission determined according to determined damage quantifier.
 3. Themethod of claim 2, wherein determining the mission includes selecting,based on the determined damage quantifier, a mode of navigation from atleast one of cargo-only, cargo and passenger, move with no cargo orpassenger, stop movement.
 4. The method of claim 3, further comprisingselecting the mode of navigation based on a plurality of damagequantifier thresholds.
 5. The method of claim 2, further comprisingpredicting a change of the damage quantifier based on at least one of(a) a planned route of the vehicle including data specifying whether theplanned route includes a rough road surface, or (b) environmental sensordata including at least one of a vehicle speed, an ambient temperature,and an ambient humidity.
 6. A vehicle comprising: a plurality ofsensors; and a computer, comprising a processor and a memory, the memorystoring instructions executable by the processor to: prior to deploymentof a vehicle, identify stress locations in a first vehicle component forone or more types of loading modes of the first vehicle component, basedon inputting the loading modes and data describing the component to asimulation that outputs the stress location; wherein respective types ofloading modes specify directional forces to the first vehicle componentwhen a plurality of vehicle components receive one or more forces froman impact to the vehicle; and identifying a plurality of sensorlocations in the vehicle component for the plurality of sensors based onthe identified stress locations in the vehicle component that specifythe directional forces to the vehicle component; and wherein theplurality of the sensors are mounted at the identified sensor locations.7. The vehicle of claim 6, wherein the instructions further includeinstructions to: upon deployment of the vehicle, determine a damagequantifier for a vehicle component formed of composite material based onvehicle sensor data; and operate the vehicle based on a missiondetermined according to determined damage quantifier.
 8. The system ofclaim 7, wherein the instructions further include instructions topredict a change of the damage quantifier based on at least one of (a) aplanned route of the vehicle including data specifying whether theplanned route includes a rough road surface, or (b) environmental sensordata including at least one of a vehicle speed, an ambient temperature,and an ambient humidity.
 9. The system of claim 7, wherein theinstructions further include instructions to determine the damagequantifier based on a model that takes the received data from thesensors included in the vehicle component as input, and outputs thedamage quantifier for the respective vehicle component.
 10. The systemof claim 7, wherein the types of loading modes include at least one of aroof impact, a frontal impact, a rear impact, and a side impact.
 11. Thesystem of claim 7, wherein the instructions to determine the missionfurther include instructions to select, based on the determined damagequantifier, a mode of navigation from at least one of cargo-only, cargoand passenger, move with no cargo or passenger, stop movement.
 12. Thesystem of claim 11, wherein the instructions further includeinstructions to select the mode of navigation based on a plurality ofdamage quantifier thresholds.
 13. The system of claim 10, wherein theinstructions further include instructions to: select one or more typesof loading modes for detection; and identify the plurality of sensorlocations based on the selected one or more types of loading modes. 14.The method of claim 2, further comprising determining a vehicle damagequantifier based on a combination of a plurality of component damagequantifiers.
 15. The method of claim 2, further comprising training aneural network for determining the damage quantifier using ground truthdata generated based on simulating an effect of the damage on physicalattributes of the component or measurement data received from a labmeasurement equipment including at least one of X-Ray and an ultrasoundscanner.
 16. The method of claim 5, wherein predicting the change of thedamage quantifier further includes predicting a first damage quantifierbased on a first safety standard, predicting a second damage quantifierbased on a second safety standard and determining the predicted damagequantifier based on the first and second damage quantifiers.
 17. Thesystem of claim 7, wherein the instructions further include instructionsto determine the damage quantifier based on a combination of a pluralityof component damage quantifiers.
 18. The system of claim 7, wherein theinstructions further include instructions to train a neural network fordetermining the damage quantifier using ground truth data generatedbased on simulating an effect of the damage on physical attributes ofthe component or measurement data received from a lab measurementequipment including at least one of X-Ray and an ultrasound scanner. 19.The system of claim 8, wherein the instructions further includeinstructions to predict the change of the damage quantifier bypredicting a first damage quantifier based on a first safety standard,predicting a second damage quantifier based on a second safety standardand determining the predicted damage quantifier based on the first andsecond damage quantifiers.
 20. The system of claim 19, wherein theinstructions further include instructions to determine the predicteddamage quantifier by determining a maximum of the first damagequantifier and the second damage quantifier.